Radio Frequency Fingerprinting (RFF) techniques allow a receiver to authenticate a transmitter by analyzing the physical layer of the radio spectrum. Although the vast majority of scientific contributions focus on improving the performance of RFF considering different parameters and scenarios, in this work, we consider RFF as an attack vector to identify a target device in the radio spectrum. \\ We propose, implement, and evaluate {\em HidePrint}, a solution to prevent identification through RFF without affecting the quality of the communication link between the transmitter and the receiver. {\em HidePrint} hides the transmitter's fingerprint against an illegitimate eavesdropper through the injection of controlled noise into the transmitted signal. We evaluate our solution against various state-of-the-art RFF techniques, considering several adversarial models, data from real-world communication links (wired and wireless), and protocol configurations. Our results show that the injection of a Gaussian noise pattern with a normalized standard deviation of (at least) 0.02 prevents device fingerprinting in all the considered scenarios, while affecting the Signal-to-Noise Ratio (SNR) of the received signal by only 0.1 dB. Moreover, we introduce {\em selective radio fingerprint disclosure}, a new technique that allows the transmitter to disclose the radio fingerprint to only a subset of intended receivers.

翻译：射频指纹识别技术允许接收方通过分析射频频谱的物理层来认证发射方。尽管绝大多数科学贡献聚焦于在不同参数和场景下提升RFF的性能，但本工作将RFF视为在射频频谱中识别目标设备的攻击向量。我们提出、实现并评估了HidePrint，这是一种在不影响发射方与接收方之间通信链路质量的前提下，防止通过RFF进行识别的解决方案。HidePrint通过向发射信号中注入受控噪声，隐藏发射方的指纹，以对抗非法窃听者。我们针对多种先进的RFF技术评估了该方案，考虑了多种对抗模型、真实世界通信链路（有线和无线）的数据以及协议配置。结果表明，注入归一化标准差至少为0.02的高斯噪声模式，可在所有考虑的场景中防止设备指纹识别，同时仅使接收信号的信噪比降低0.1 dB。此外，我们引入了选择性射频指纹披露技术，该技术允许发射方仅向部分目标接收方披露射频指纹。